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case-studies/cognida

Finding subrogation cases a rule engine missed

role
Software/AI Developer, Cognida.ai, Hyderabad, India
when
Jul 2022 – Aug 2024
stack
Python, PySpark, topic modeling, K-means, SQL
Context

Subrogation means recovering a claim payment from the party actually responsible for it. The client screened health insurance claims with a rule engine, which caught the obvious cases and missed the rest.

Approach
  1. 1PySpark feature engineering over millions of claims
  2. 2Topic modeling over diagnosis and procedure codes, so related codes group together
  3. 3K-means clustering on the combined features
  4. 4A distance-based anomaly score: claims far from their cluster are flagged for review
Result
+8%additional new cases flagged for investigation

Shipped to production alongside the existing rule engine.

Other work there
  • ·Retrieval and embedding-based RAG for financial document search, evaluated on retrieval accuracy
  • ·Computer vision and OCR classification models for an AI inspection platform, monitored in production
  • ·Fine-tuned and quantized an LLM for an insurance-company chatbot to lower serving cost
  • ·Compared AWS and Azure for model serving, and helped set up Git, CI/CD and MLOps practices